Overview

Dataset statistics

Number of variables32
Number of observations1296675
Missing cells1318
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory294.3 MiB
Average record size in memory238.0 B

Variable types

DateTime1
Numeric16
Categorical13
Unsupported2

Alerts

merchant has a high cardinality: 693 distinct values High cardinality
street has a high cardinality: 983 distinct values High cardinality
city has a high cardinality: 894 distinct values High cardinality
state has a high cardinality: 51 distinct values High cardinality
job has a high cardinality: 494 distinct values High cardinality
trans_num has a high cardinality: 1296675 distinct values High cardinality
name has a high cardinality: 973 distinct values High cardinality
lat is highly correlated with merch_latHigh correlation
long is highly correlated with merch_longHigh correlation
merch_lat is highly correlated with latHigh correlation
merch_long is highly correlated with longHigh correlation
trans_month is highly correlated with trans_weekHigh correlation
trans_week is highly correlated with trans_monthHigh correlation
lat is highly correlated with merch_latHigh correlation
long is highly correlated with merch_longHigh correlation
merch_lat is highly correlated with latHigh correlation
merch_long is highly correlated with longHigh correlation
trans_month is highly correlated with trans_weekHigh correlation
trans_week is highly correlated with trans_monthHigh correlation
lat is highly correlated with merch_latHigh correlation
long is highly correlated with merch_longHigh correlation
merch_lat is highly correlated with latHigh correlation
merch_long is highly correlated with longHigh correlation
trans_month is highly correlated with trans_weekHigh correlation
trans_week is highly correlated with trans_monthHigh correlation
category is highly correlated with trans_hour and 1 other fieldsHigh correlation
state is highly correlated with zip and 7 other fieldsHigh correlation
zip is highly correlated with state and 4 other fieldsHigh correlation
lat is highly correlated with state and 4 other fieldsHigh correlation
long is highly correlated with state and 4 other fieldsHigh correlation
city_pop is highly correlated with stateHigh correlation
merch_lat is highly correlated with state and 4 other fieldsHigh correlation
merch_long is highly correlated with state and 4 other fieldsHigh correlation
trans_year is highly correlated with trans_month and 1 other fieldsHigh correlation
trans_month is highly correlated with trans_year and 1 other fieldsHigh correlation
trans_week is highly correlated with trans_year and 1 other fieldsHigh correlation
trans_hour is highly correlated with categoryHigh correlation
age is highly correlated with state and 1 other fieldsHigh correlation
amt_group is highly correlated with categoryHigh correlation
age_group is highly correlated with state and 1 other fieldsHigh correlation
amt is highly skewed (γ1 = 42.27787379) Skewed
trans_num is uniformly distributed Uniform
trans_num has unique values Unique
distance has unique values Unique
coords_ori is an unsupported type, check if it needs cleaning or further analysis Unsupported
coords_merch is an unsupported type, check if it needs cleaning or further analysis Unsupported
trans_hour has 42502 (3.3%) zeros Zeros
trans_minute has 21372 (1.6%) zeros Zeros
trans_dayofweek has 254282 (19.6%) zeros Zeros

Reproduction

Analysis started2022-03-02 14:44:43.186655
Analysis finished2022-03-02 14:48:23.428924
Duration3 minutes and 40.24 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Distinct1274791
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
Minimum2019-01-01 00:00:18
Maximum2020-06-21 12:13:37
2022-03-02T22:48:23.496923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:48:23.582924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

cc_num
Real number (ℝ≥0)

Distinct983
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.171920421 × 1017
Minimum6.041620718 × 1010
Maximum4.992346398 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2022-03-02T22:48:23.668923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum6.041620718 × 1010
5-th percentile6.304848798 × 1011
Q11.800429465 × 1014
median3.521417321 × 1015
Q34.642255475 × 1015
95-th percentile4.497913966 × 1018
Maximum4.992346398 × 1018
Range4.992346338 × 1018
Interquartile range (IQR)4.462212529 × 1015

Descriptive statistics

Standard deviation1.308806447 × 1018
Coefficient of variation (CV)3.1371798
Kurtosis6.179949935
Mean4.171920421 × 1017
Median Absolute Deviation (MAD)3.076470873 × 1015
Skewness2.851879006
Sum-6.725541877 × 1018
Variance1.712974316 × 1036
MonotonicityNot monotonic
2022-03-02T22:48:23.752923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.713652351 × 10113123
 
0.2%
4.512828415 × 10183123
 
0.2%
3.672269902 × 10133119
 
0.2%
2.131124026 × 10143117
 
0.2%
3.54510934 × 10153113
 
0.2%
6.534628261 × 10153112
 
0.2%
6.011367958 × 10153110
 
0.2%
2.720433096 × 10153107
 
0.2%
6.011438889 × 10153106
 
0.2%
6.011109737 × 10153101
 
0.2%
Other values (973)1265544
97.6%
ValueCountFrequency (%)
6.041620718 × 10101518
0.1%
6.042292873 × 10101531
0.1%
6.042309813 × 1010510
 
< 0.1%
6.042785159 × 1010528
 
< 0.1%
6.048700208 × 1010496
 
< 0.1%
6.04905963 × 10101010
0.1%
6.049559311 × 1010518
 
< 0.1%
5.018029536 × 10111559
0.1%
5.018181333 × 10118
 
< 0.1%
5.018282048 × 1011515
 
< 0.1%
ValueCountFrequency (%)
4.992346398 × 10182059
0.2%
4.989847571 × 10181007
 
0.1%
4.980323468 × 1018532
 
< 0.1%
4.973530368 × 10181040
0.1%
4.958589672 × 10181476
0.1%
4.95682899 × 10182566
0.2%
4.911818931 × 10189
 
< 0.1%
4.906628656 × 10182584
0.2%
4.897067971 × 10181038
0.1%
4.890424427 × 10181496
0.1%

merchant
Categorical

HIGH CARDINALITY

Distinct693
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
fraud_Kilback_LLC
 
4403
fraud_Cormier_LLC
 
3649
fraud_Schumm_PLC
 
3634
fraud_Kuhn_LLC
 
3510
fraud_Boyer_PLC
 
3493
Other values (688)
1277986 

Length

Max length43
Median length20
Mean length23.13259683
Min length13

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfraud_Rippin,_Kub_and_Mann
2nd rowfraud_Heller,_Gutmann_and_Zieme
3rd rowfraud_Lind-Buckridge
4th rowfraud_Kutch,_Hermiston_and_Farrell
5th rowfraud_Keeling-Crist

Common Values

ValueCountFrequency (%)
fraud_Kilback_LLC4403
 
0.3%
fraud_Cormier_LLC3649
 
0.3%
fraud_Schumm_PLC3634
 
0.3%
fraud_Kuhn_LLC3510
 
0.3%
fraud_Boyer_PLC3493
 
0.3%
fraud_Dickinson_Ltd3434
 
0.3%
fraud_Cummerata-Jones2736
 
0.2%
fraud_Kutch_LLC2734
 
0.2%
fraud_Olson,_Becker_and_Koch2723
 
0.2%
fraud_Stroman,_Hudson_and_Erdman2721
 
0.2%
Other values (683)1263638
97.5%

Length

2022-03-02T22:48:23.835923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fraud_kilback_llc4403
 
0.3%
fraud_cormier_llc3649
 
0.3%
fraud_schumm_plc3634
 
0.3%
fraud_kuhn_llc3510
 
0.3%
fraud_boyer_plc3493
 
0.3%
fraud_dickinson_ltd3434
 
0.3%
fraud_cummerata-jones2736
 
0.2%
fraud_kutch_llc2734
 
0.2%
fraud_olson,_becker_and_koch2723
 
0.2%
fraud_stroman,_hudson_and_erdman2721
 
0.2%
Other values (683)1263638
97.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

category
Categorical

HIGH CORRELATION

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
gas_transport
131659 
grocery_pos
123638 
home
123115 
shopping_pos
116672 
kids_pets
113035 
Other values (9)
688556 

Length

Max length14
Median length11
Mean length10.52607862
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmisc_net
2nd rowgrocery_pos
3rd rowentertainment
4th rowgas_transport
5th rowmisc_pos

Common Values

ValueCountFrequency (%)
gas_transport131659
10.2%
grocery_pos123638
9.5%
home123115
9.5%
shopping_pos116672
9.0%
kids_pets113035
8.7%
shopping_net97543
7.5%
entertainment94014
7.3%
food_dining91461
 
7.1%
personal_care90758
 
7.0%
health_fitness85879
 
6.6%
Other values (4)228901
17.7%

Length

2022-03-02T22:48:23.908923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gas_transport131659
10.2%
grocery_pos123638
9.5%
home123115
9.5%
shopping_pos116672
9.0%
kids_pets113035
8.7%
shopping_net97543
7.5%
entertainment94014
7.3%
food_dining91461
 
7.1%
personal_care90758
 
7.0%
health_fitness85879
 
6.6%
Other values (4)228901
17.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

amt
Real number (ℝ≥0)

SKEWED

Distinct52928
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.35103546
Minimum1
Maximum28948.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2022-03-02T22:48:23.978923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.44
Q19.65
median47.52
Q383.14
95-th percentile196.31
Maximum28948.9
Range28947.9
Interquartile range (IQR)73.49

Descriptive statistics

Standard deviation160.3160386
Coefficient of variation (CV)2.278801407
Kurtosis4545.644979
Mean70.35103546
Median Absolute Deviation (MAD)37.5
Skewness42.27787379
Sum91222428.9
Variance25701.23222
MonotonicityNot monotonic
2022-03-02T22:48:24.055923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.14542
 
< 0.1%
1.04538
 
< 0.1%
1.25535
 
< 0.1%
1.02533
 
< 0.1%
1.01523
 
< 0.1%
1.05519
 
< 0.1%
1.2516
 
< 0.1%
1.23515
 
< 0.1%
1.08512
 
< 0.1%
1.11509
 
< 0.1%
Other values (52918)1291433
99.6%
ValueCountFrequency (%)
1222
< 0.1%
1.01523
< 0.1%
1.02533
< 0.1%
1.03499
< 0.1%
1.04538
< 0.1%
1.05519
< 0.1%
1.06471
< 0.1%
1.07498
< 0.1%
1.08512
< 0.1%
1.09496
< 0.1%
ValueCountFrequency (%)
28948.91
< 0.1%
27390.121
< 0.1%
27119.771
< 0.1%
26544.121
< 0.1%
25086.941
< 0.1%
17897.241
< 0.1%
15305.951
< 0.1%
15047.031
< 0.1%
15034.181
< 0.1%
14849.741
< 0.1%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
F
709863 
M
586812 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
F709863
54.7%
M586812
45.3%

Length

2022-03-02T22:48:24.128923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-02T22:48:24.167923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
f709863
54.7%
m586812
45.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

street
Categorical

HIGH CARDINALITY

Distinct983
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
0069 Robin Brooks Apt. 695
 
3123
864 Reynolds Plains
 
3123
8172 Robertson Parkways Suite 072
 
3119
4664 Sanchez Common Suite 930
 
3117
8030 Beck Motorway
 
3113
Other values (978)
1281080 

Length

Max length35
Median length22
Mean length22.22902655
Min length12

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row561 Perry Cove
2nd row43039 Riley Greens Suite 393
3rd row594 White Dale Suite 530
4th row9443 Cynthia Court Apt. 038
5th row408 Bradley Rest

Common Values

ValueCountFrequency (%)
0069 Robin Brooks Apt. 6953123
 
0.2%
864 Reynolds Plains3123
 
0.2%
8172 Robertson Parkways Suite 0723119
 
0.2%
4664 Sanchez Common Suite 9303117
 
0.2%
8030 Beck Motorway3113
 
0.2%
29606 Martinez Views Suite 6533112
 
0.2%
1652 James Mews3110
 
0.2%
854 Walker Dale Suite 4883107
 
0.2%
40624 Rebecca Spurs3106
 
0.2%
594 Berry Lights Apt. 3923101
 
0.2%
Other values (973)1265544
97.6%

Length

2022-03-02T22:48:24.216923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
apt327791
 
6.4%
suite305467
 
5.9%
island22954
 
0.4%
michael18967
 
0.4%
common17978
 
0.3%
station17957
 
0.3%
islands17917
 
0.3%
david17476
 
0.3%
brooks16991
 
0.3%
fields16321
 
0.3%
Other values (1940)4376722
84.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

city
Categorical

HIGH CARDINALITY

Distinct894
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
Birmingham
 
5617
San_Antonio
 
5130
Utica
 
5105
Phoenix
 
5075
Meridian
 
5060
Other values (889)
1270688 

Length

Max length25
Median length8
Mean length8.652245937
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMoravian_Falls
2nd rowOrient
3rd rowMalad_City
4th rowBoulder
5th rowDoe_Hill

Common Values

ValueCountFrequency (%)
Birmingham5617
 
0.4%
San_Antonio5130
 
0.4%
Utica5105
 
0.4%
Phoenix5075
 
0.4%
Meridian5060
 
0.4%
Thomas4634
 
0.4%
Conway4613
 
0.4%
Cleveland4604
 
0.4%
Warren4599
 
0.4%
Houston4168
 
0.3%
Other values (884)1248070
96.3%

Length

2022-03-02T22:48:24.287924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
birmingham5617
 
0.4%
san_antonio5130
 
0.4%
utica5105
 
0.4%
phoenix5075
 
0.4%
meridian5060
 
0.4%
thomas4634
 
0.4%
conway4613
 
0.4%
cleveland4604
 
0.4%
warren4599
 
0.4%
houston4168
 
0.3%
Other values (884)1248070
96.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

state
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
TX
94876 
NY
 
83501
PA
 
79847
CA
 
56360
OH
 
46480
Other values (46)
935611 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNC
2nd rowWA
3rd rowID
4th rowMT
5th rowVA

Common Values

ValueCountFrequency (%)
TX94876
 
7.3%
NY83501
 
6.4%
PA79847
 
6.2%
CA56360
 
4.3%
OH46480
 
3.6%
MI46154
 
3.6%
IL43252
 
3.3%
FL42671
 
3.3%
AL40989
 
3.2%
MO38403
 
3.0%
Other values (41)724142
55.8%

Length

2022-03-02T22:48:24.348924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tx94876
 
7.3%
ny83501
 
6.4%
pa79847
 
6.2%
ca56360
 
4.3%
oh46480
 
3.6%
mi46154
 
3.6%
il43252
 
3.3%
fl42671
 
3.3%
al40989
 
3.2%
mo38403
 
3.0%
Other values (41)724142
55.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

zip
Real number (ℝ≥0)

HIGH CORRELATION

Distinct970
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48800.6711
Minimum1257
Maximum99783
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2022-03-02T22:48:24.416923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1257
5-th percentile7208
Q126237
median48174
Q372042
95-th percentile94569
Maximum99783
Range98526
Interquartile range (IQR)45805

Descriptive statistics

Standard deviation26893.22248
Coefficient of variation (CV)0.551083046
Kurtosis-1.096449332
Mean48800.6711
Median Absolute Deviation (MAD)23068
Skewness0.07968075775
Sum6.32786102 × 1010
Variance723245415.2
MonotonicityNot monotonic
2022-03-02T22:48:24.496924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
737543646
 
0.3%
341123613
 
0.3%
480883597
 
0.3%
825143527
 
0.3%
496283123
 
0.2%
154843123
 
0.2%
851733119
 
0.2%
298193117
 
0.2%
387613113
 
0.2%
54613112
 
0.2%
Other values (960)1263585
97.4%
ValueCountFrequency (%)
12572023
0.2%
13301031
 
0.1%
1535515
 
< 0.1%
15451024
 
0.1%
1612519
 
< 0.1%
18432597
0.2%
18442058
0.2%
2180519
 
< 0.1%
26302090
0.2%
2908550
 
< 0.1%
ValueCountFrequency (%)
997831568
0.1%
9974712
 
< 0.1%
99746540
 
< 0.1%
993232572
0.2%
991603030
0.2%
9911615
 
< 0.1%
991131047
 
0.1%
990332458
0.2%
98836524
 
< 0.1%
98665500
 
< 0.1%

lat
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct968
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.53762161
Minimum20.0271
Maximum66.6933
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2022-03-02T22:48:24.573923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20.0271
5-th percentile29.8826
Q134.6205
median39.3543
Q341.9404
95-th percentile45.8433
Maximum66.6933
Range46.6662
Interquartile range (IQR)7.3199

Descriptive statistics

Standard deviation5.075808439
Coefficient of variation (CV)0.1317104748
Kurtosis0.8129679455
Mean38.53762161
Median Absolute Deviation (MAD)3.3597
Skewness-0.1860276801
Sum49970770.51
Variance25.76383131
MonotonicityNot monotonic
2022-03-02T22:48:24.652924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.3853646
 
0.3%
26.11843613
 
0.3%
42.51643597
 
0.3%
43.00483527
 
0.3%
39.89363123
 
0.2%
44.59953123
 
0.2%
33.28873119
 
0.2%
34.03263117
 
0.2%
33.47833113
 
0.2%
44.33463112
 
0.2%
Other values (958)1263585
97.4%
ValueCountFrequency (%)
20.02711527
0.1%
20.08271032
 
0.1%
24.65572584
0.2%
26.11843613
0.3%
26.3304542
 
< 0.1%
26.3771518
 
< 0.1%
26.42153038
0.2%
26.47222524
0.2%
26.5291549
0.1%
26.69391027
 
0.1%
ValueCountFrequency (%)
66.693312
 
< 0.1%
65.6899540
 
< 0.1%
64.75561568
0.1%
48.88783030
0.2%
48.88562066
0.2%
48.83281533
0.1%
48.66691047
 
0.1%
48.60312973
0.2%
48.47862038
0.2%
48.343088
0.2%

long
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct969
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-90.22633538
Minimum-165.6723
Maximum-67.9503
Zeros0
Zeros (%)0.0%
Negative1296675
Negative (%)100.0%
Memory size9.9 MiB
2022-03-02T22:48:24.734924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-165.6723
5-th percentile-119.0825
Q1-96.798
median-87.4769
Q3-80.158
95-th percentile-73.5112
Maximum-67.9503
Range97.722
Interquartile range (IQR)16.64

Descriptive statistics

Standard deviation13.75907695
Coefficient of variation (CV)-0.1524951323
Kurtosis1.855892285
Mean-90.22633538
Median Absolute Deviation (MAD)8.1527
Skewness-1.150107737
Sum-116994233.4
Variance189.3121984
MonotonicityNot monotonic
2022-03-02T22:48:24.812924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-98.07273646
 
0.3%
-81.73613613
 
0.3%
-82.98323597
 
0.3%
-108.89643527
 
0.3%
-79.78563123
 
0.2%
-86.21413123
 
0.2%
-111.09853119
 
0.2%
-82.20273117
 
0.2%
-90.51423113
 
0.2%
-73.0983112
 
0.2%
Other values (959)1263585
97.4%
ValueCountFrequency (%)
-165.67231568
0.1%
-156.292540
 
< 0.1%
-155.4881032
0.1%
-155.36971527
0.1%
-153.99412
 
< 0.1%
-124.44091043
0.1%
-124.21741547
0.1%
-124.15871031
0.1%
-124.14371526
0.1%
-123.97432036
0.2%
ValueCountFrequency (%)
-67.95032080
0.2%
-68.55651014
 
0.1%
-69.2675519
 
< 0.1%
-69.48282050
0.2%
-69.9576537
 
< 0.1%
-69.96563107
0.2%
-70.10319
 
< 0.1%
-70.2391036
 
0.1%
-70.30012090
0.2%
-70.34571527
0.1%

city_pop
Real number (ℝ≥0)

HIGH CORRELATION

Distinct879
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88824.44056
Minimum23
Maximum2906700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2022-03-02T22:48:24.996923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile139
Q1743
median2456
Q320328
95-th percentile525713
Maximum2906700
Range2906677
Interquartile range (IQR)19585

Descriptive statistics

Standard deviation301956.3607
Coefficient of variation (CV)3.399473825
Kurtosis37.6145193
Mean88824.44056
Median Absolute Deviation (MAD)2198
Skewness5.593853067
Sum1.151764315 × 1011
Variance9.117764376 × 1010
MonotonicityNot monotonic
2022-03-02T22:48:25.072924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6065496
 
0.4%
15957975130
 
0.4%
13129225075
 
0.4%
17664574
 
0.4%
2414533
 
0.3%
29067004168
 
0.3%
2760024155
 
0.3%
3024147
 
0.3%
9101484073
 
0.3%
1984067
 
0.3%
Other values (869)1251257
96.5%
ValueCountFrequency (%)
232049
0.2%
371013
 
0.1%
432034
0.2%
463040
0.2%
47511
 
< 0.1%
491054
 
0.1%
511016
 
0.1%
52518
 
< 0.1%
532610
0.2%
601045
 
0.1%
ValueCountFrequency (%)
29067004168
0.3%
25047002033
 
0.2%
2383912521
 
< 0.1%
15957975130
0.4%
15773852563
0.2%
15262063517
0.3%
14177938
 
< 0.1%
13824802056
0.2%
13129225075
0.4%
12633213629
0.3%

job
Categorical

HIGH CARDINALITY

Distinct494
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
Film/video editor
 
9779
Exhibition designer
 
9199
Naval architect
 
8684
Surveyor, land/geomatics
 
8680
Materials engineer
 
8270
Other values (489)
1252063 

Length

Max length59
Median length19
Mean length20.2271024
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPsychologist, counselling
2nd rowSpecial educational needs teacher
3rd rowNature conservation officer
4th rowPatent attorney
5th rowDance movement psychotherapist

Common Values

ValueCountFrequency (%)
Film/video editor9779
 
0.8%
Exhibition designer9199
 
0.7%
Naval architect8684
 
0.7%
Surveyor, land/geomatics8680
 
0.7%
Materials engineer8270
 
0.6%
Designer, ceramics/pottery8225
 
0.6%
Systems developer7700
 
0.6%
IT trainer7679
 
0.6%
Financial adviser7659
 
0.6%
Environmental consultant7547
 
0.6%
Other values (484)1213253
93.6%

Length

2022-03-02T22:48:25.160924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
engineer131756
 
4.6%
officer110915
 
3.9%
manager61124
 
2.1%
scientist55878
 
1.9%
designer52218
 
1.8%
surveyor49062
 
1.7%
teacher38126
 
1.3%
psychologist32600
 
1.1%
research29754
 
1.0%
editor28725
 
1.0%
Other values (456)2289024
79.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

trans_num
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct1296675
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
0b242abb623afc578575680df30655b9
 
1
c85864e7e7cf0be6d1b8597977b8afea
 
1
1a8a2a05638a5503cc6bb8d5735efcc1
 
1
4556eaf1f7def06eb500325cde4d054e
 
1
5e915d9f88bd09cee9655a470d9bc0bd
 
1
Other values (1296670)
1296670 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1296675 ?
Unique (%)100.0%

Sample

1st row0b242abb623afc578575680df30655b9
2nd row1f76529f8574734946361c461b024d99
3rd rowa1a22d70485983eac12b5b88dad1cf95
4th row6b849c168bdad6f867558c3793159a81
5th rowa41d7549acf90789359a9aa5346dcb46

Common Values

ValueCountFrequency (%)
0b242abb623afc578575680df30655b91
 
< 0.1%
c85864e7e7cf0be6d1b8597977b8afea1
 
< 0.1%
1a8a2a05638a5503cc6bb8d5735efcc11
 
< 0.1%
4556eaf1f7def06eb500325cde4d054e1
 
< 0.1%
5e915d9f88bd09cee9655a470d9bc0bd1
 
< 0.1%
4e0080ea32b67dc251ea824d55ba1f6f1
 
< 0.1%
541a9a3880dae40c9e7778117adbc89f1
 
< 0.1%
2c602fbe0404b65cc431b059ed1675181
 
< 0.1%
6f9d22d80c0c48e238ecc484d1c64a491
 
< 0.1%
c766663cba6e1a1df3623e4f9d6472de1
 
< 0.1%
Other values (1296665)1296665
> 99.9%

Length

2022-03-02T22:48:25.285924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0b242abb623afc578575680df30655b91
 
< 0.1%
c1d9a7ddb1e34639fe82758de97f4abf1
 
< 0.1%
189a841a0a8ba03058526bcfe566aab51
 
< 0.1%
83ec1cc84142af6e2acf10c44949e7201
 
< 0.1%
6d294ed2cc447d2c71c7171a3d54967c1
 
< 0.1%
fc28024ce480f8ef21a32d64c93a29f51
 
< 0.1%
7bb25a43205191eb7344282b88fc54d31
 
< 0.1%
3b9014ea8fb80bd65de0b1463b00b00e1
 
< 0.1%
3c74776e558f1499a7824b556e474b1d1
 
< 0.1%
413636e759663f264aae1819a4d4f2311
 
< 0.1%
Other values (1296665)1296665
> 99.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

merch_lat
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1247805
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.53733804
Minimum19.027785
Maximum67.510267
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2022-03-02T22:48:25.358924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum19.027785
5-th percentile29.7516534
Q134.733572
median39.36568
Q341.957164
95-th percentile46.0035301
Maximum67.510267
Range48.482482
Interquartile range (IQR)7.223592

Descriptive statistics

Standard deviation5.10978837
Coefficient of variation (CV)0.1325931844
Kurtosis0.79599391
Mean38.53733804
Median Absolute Deviation (MAD)3.397536
Skewness-0.1819154297
Sum49970402.81
Variance26.10993718
MonotonicityNot monotonic
2022-03-02T22:48:25.436924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.3059664
 
< 0.1%
41.9377964
 
< 0.1%
42.2650124
 
< 0.1%
41.3016114
 
< 0.1%
34.1349944
 
< 0.1%
37.6697884
 
< 0.1%
39.3481854
 
< 0.1%
32.644694
 
< 0.1%
42.7491844
 
< 0.1%
38.0506734
 
< 0.1%
Other values (1247795)1296635
> 99.9%
ValueCountFrequency (%)
19.0277851
< 0.1%
19.0278041
< 0.1%
19.0297981
< 0.1%
19.0312421
< 0.1%
19.0322771
< 0.1%
19.0332881
< 0.1%
19.0342821
< 0.1%
19.0346871
< 0.1%
19.0354721
< 0.1%
19.0363121
< 0.1%
ValueCountFrequency (%)
67.5102671
< 0.1%
67.4415181
< 0.1%
67.3970181
< 0.1%
67.1881111
< 0.1%
67.0642771
< 0.1%
66.8351741
< 0.1%
66.6829051
< 0.1%
66.673551
< 0.1%
66.6646731
< 0.1%
66.6592421
< 0.1%

merch_long
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1275745
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-90.2264648
Minimum-166.671242
Maximum-66.950902
Zeros0
Zeros (%)0.0%
Negative1296675
Negative (%)100.0%
Memory size9.9 MiB
2022-03-02T22:48:25.518924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-166.671242
5-th percentile-119.3300916
Q1-96.8972755
median-87.438392
Q3-80.2367965
95-th percentile-73.3542179
Maximum-66.950902
Range99.72034
Interquartile range (IQR)16.660479

Descriptive statistics

Standard deviation13.77109056
Coefficient of variation (CV)-0.1526280631
Kurtosis1.848479176
Mean-90.2264648
Median Absolute Deviation (MAD)8.227889
Skewness-1.146959945
Sum-116994401.2
Variance189.6429353
MonotonicityNot monotonic
2022-03-02T22:48:25.595924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-87.1164144
 
< 0.1%
-81.2191894
 
< 0.1%
-74.6182694
 
< 0.1%
-85.3263233
 
< 0.1%
-84.8903053
 
< 0.1%
-88.493093
 
< 0.1%
-84.1001023
 
< 0.1%
-97.5272273
 
< 0.1%
-85.34443
 
< 0.1%
-86.0374943
 
< 0.1%
Other values (1275735)1296642
> 99.9%
ValueCountFrequency (%)
-166.6712421
< 0.1%
-166.6701321
< 0.1%
-166.6696381
< 0.1%
-166.6661791
< 0.1%
-166.6648281
< 0.1%
-166.6628881
< 0.1%
-166.6619681
< 0.1%
-166.6592771
< 0.1%
-166.6578341
< 0.1%
-166.6571741
< 0.1%
ValueCountFrequency (%)
-66.9509021
< 0.1%
-66.9559961
< 0.1%
-66.956541
< 0.1%
-66.9586591
< 0.1%
-66.9587511
< 0.1%
-66.9591781
< 0.1%
-66.9619231
< 0.1%
-66.9629131
< 0.1%
-66.9639181
< 0.1%
-66.9639751
< 0.1%

is_fraud
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
0
1289169 
1
 
7506

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01289169
99.4%
17506
 
0.6%

Length

2022-03-02T22:48:25.665925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-02T22:48:25.706925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
01289169
99.4%
17506
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

name
Categorical

HIGH CARDINALITY

Distinct973
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
Scott Martin
 
4618
Jeffrey Smith
 
3592
Barbara Taylor
 
3123
Monica Cohen
 
3123
Jessica Perez
 
3119
Other values (968)
1279100 

Length

Max length21
Median length13
Mean length13.19160931
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJennifer Banks
2nd rowStephanie Gill
3rd rowEdward Sanchez
4th rowJeremy White
5th rowTyler Garcia

Common Values

ValueCountFrequency (%)
Scott Martin4618
 
0.4%
Jeffrey Smith3592
 
0.3%
Barbara Taylor3123
 
0.2%
Monica Cohen3123
 
0.2%
Jessica Perez3119
 
0.2%
Ana Howell3117
 
0.2%
Keith Sanders3113
 
0.2%
Christine Harris3112
 
0.2%
Tammy Ayers3110
 
0.2%
Mark Wood3107
 
0.2%
Other values (963)1263541
97.4%

Length

2022-03-02T22:48:25.751924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
smith28794
 
1.1%
christopher26669
 
1.0%
williams23605
 
0.9%
james22073
 
0.9%
davis21910
 
0.8%
robert21667
 
0.8%
jessica20581
 
0.8%
johnson20034
 
0.8%
michael20009
 
0.8%
david19965
 
0.8%
Other values (796)2368043
91.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

coords_ori
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size9.9 MiB

coords_merch
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size9.9 MiB

trans_year
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
2019
924850 
2020
371825 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019924850
71.3%
2020371825
28.7%

Length

2022-03-02T22:48:25.817925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-02T22:48:25.856925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2019924850
71.3%
2020371825
28.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

trans_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.142149729
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2022-03-02T22:48:25.896925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.417703308
Coefficient of variation (CV)0.5564343852
Kurtosis-1.04754632
Mean6.142149729
Median Absolute Deviation (MAD)3
Skewness0.298515751
Sum7964372
Variance11.6806959
MonotonicityNot monotonic
2022-03-02T22:48:25.952925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
5146875
11.3%
6143811
11.1%
3143789
11.1%
12141060
10.9%
4134970
10.4%
1104727
8.1%
297657
7.5%
887359
6.7%
786596
6.7%
970652
5.4%
Other values (2)139179
10.7%
ValueCountFrequency (%)
1104727
8.1%
297657
7.5%
3143789
11.1%
4134970
10.4%
5146875
11.3%
6143811
11.1%
786596
6.7%
887359
6.7%
970652
5.4%
1068758
5.3%
ValueCountFrequency (%)
12141060
10.9%
1170421
5.4%
1068758
5.3%
970652
5.4%
887359
6.7%
786596
6.7%
6143811
11.1%
5146875
11.3%
4134970
10.4%
3143789
11.1%

trans_week
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.71640002
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2022-03-02T22:48:26.021925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113
median23
Q336
95-th percentile50
Maximum52
Range51
Interquartile range (IQR)23

Descriptive statistics

Standard deviation14.82039325
Coefficient of variation (CV)0.5996177937
Kurtosis-1.011118764
Mean24.71640002
Median Absolute Deviation (MAD)11
Skewness0.3086148525
Sum32049138
Variance219.6440561
MonotonicityNot monotonic
2022-03-02T22:48:26.101925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2439824
 
3.1%
2339508
 
3.0%
2239465
 
3.0%
2537594
 
2.9%
2134093
 
2.6%
1232014
 
2.5%
1431874
 
2.5%
1031843
 
2.5%
1631834
 
2.5%
1531776
 
2.5%
Other values (42)946850
73.0%
ValueCountFrequency (%)
127187
2.1%
224072
1.9%
324108
1.9%
423745
1.8%
524027
1.9%
623834
1.8%
723903
1.8%
824012
1.9%
926779
2.1%
1031843
2.5%
ValueCountFrequency (%)
5231281
2.4%
5131360
2.4%
5031606
2.4%
4931570
2.4%
4821677
1.7%
4715720
1.2%
4615679
1.2%
4515828
1.2%
4415968
1.2%
4315933
1.2%

trans_day
Real number (ℝ≥0)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.58797848
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2022-03-02T22:48:26.174925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.829121359
Coefficient of variation (CV)0.5664057959
Kurtosis-1.187141658
Mean15.58797848
Median Absolute Deviation (MAD)8
Skewness0.03084736374
Sum20212542
Variance77.95338397
MonotonicityNot monotonic
2022-03-02T22:48:26.238925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
147089
 
3.6%
1546213
 
3.6%
846201
 
3.6%
1644894
 
3.5%
244748
 
3.5%
944685
 
3.4%
744239
 
3.4%
1444015
 
3.4%
2843470
 
3.4%
1742272
 
3.3%
Other values (21)848849
65.5%
ValueCountFrequency (%)
147089
3.6%
244748
3.5%
341842
3.2%
441479
3.2%
541886
3.2%
641420
3.2%
744239
3.4%
846201
3.6%
944685
3.4%
1041934
3.2%
ValueCountFrequency (%)
3124701
1.9%
3041019
3.2%
2939617
3.1%
2843470
3.4%
2739684
3.1%
2640692
3.1%
2540374
3.1%
2441360
3.2%
2340815
3.1%
2242061
3.2%

trans_hour
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.80485781
Minimum0
Maximum23
Zeros42502
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2022-03-02T22:48:26.303925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median14
Q319
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.817823899
Coefficient of variation (CV)0.5324404223
Kurtosis-1.079580292
Mean12.80485781
Median Absolute Deviation (MAD)5
Skewness-0.2828254537
Sum16603739
Variance46.48272272
MonotonicityNot monotonic
2022-03-02T22:48:26.366925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2367104
 
5.2%
2266982
 
5.2%
1866051
 
5.1%
1665726
 
5.1%
2165533
 
5.1%
1965508
 
5.1%
1765450
 
5.0%
1565391
 
5.0%
1365314
 
5.0%
1265257
 
5.0%
Other values (14)638359
49.2%
ValueCountFrequency (%)
042502
3.3%
142869
3.3%
242656
3.3%
342769
3.3%
441863
3.2%
542171
3.3%
642300
3.3%
742203
3.3%
842505
3.3%
942185
3.3%
ValueCountFrequency (%)
2367104
5.2%
2266982
5.2%
2165533
5.1%
2065098
5.0%
1965508
5.1%
1866051
5.1%
1765450
5.0%
1665726
5.1%
1565391
5.0%
1464885
5.0%

trans_minute
Real number (ℝ≥0)

ZEROS

Distinct60
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.49528525
Minimum0
Maximum59
Zeros21372
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2022-03-02T22:48:26.545925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q114
median30
Q344
95-th percentile57
Maximum59
Range59
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.32017992
Coefficient of variation (CV)0.5872185935
Kurtosis-1.200804645
Mean29.49528525
Median Absolute Deviation (MAD)15
Skewness-0.000393712934
Sum38245799
Variance299.9886324
MonotonicityNot monotonic
2022-03-02T22:48:26.619927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4021918
 
1.7%
121867
 
1.7%
721827
 
1.7%
5921803
 
1.7%
321797
 
1.7%
1421783
 
1.7%
3621779
 
1.7%
5121777
 
1.7%
2721763
 
1.7%
421748
 
1.7%
Other values (50)1078613
83.2%
ValueCountFrequency (%)
021372
1.6%
121867
1.7%
221718
1.7%
321797
1.7%
421748
1.7%
521505
1.7%
621490
1.7%
721827
1.7%
821586
1.7%
921512
1.7%
ValueCountFrequency (%)
5921803
1.7%
5821511
1.7%
5721651
1.7%
5621496
1.7%
5521474
1.7%
5421525
1.7%
5321530
1.7%
5221560
1.7%
5121777
1.7%
5021612
1.7%

trans_dayofweek
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.070603659
Minimum0
Maximum6
Zeros254282
Zeros (%)19.6%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2022-03-02T22:48:26.682927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.198152555
Coefficient of variation (CV)0.7158698414
Kurtosis-1.445048986
Mean3.070603659
Median Absolute Deviation (MAD)2
Skewness-0.07845304063
Sum3981575
Variance4.831874654
MonotonicityNot monotonic
2022-03-02T22:48:26.732927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0254282
19.6%
6250579
19.3%
5200957
15.5%
1160227
12.4%
4152272
11.7%
3147285
11.4%
2131073
10.1%
ValueCountFrequency (%)
0254282
19.6%
1160227
12.4%
2131073
10.1%
3147285
11.4%
4152272
11.7%
5200957
15.5%
6250579
19.3%
ValueCountFrequency (%)
6250579
19.3%
5200957
15.5%
4152272
11.7%
3147285
11.4%
2131073
10.1%
1160227
12.4%
0254282
19.6%

age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct83
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.02929801
Minimum14
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2022-03-02T22:48:26.802926image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile22
Q133
median44
Q357
95-th percentile80
Maximum96
Range82
Interquartile range (IQR)24

Descriptive statistics

Standard deviation17.38237262
Coefficient of variation (CV)0.3776371436
Kurtosis-0.1760038548
Mean46.02929801
Median Absolute Deviation (MAD)12
Skewness0.6122620439
Sum59685040
Variance302.1468781
MonotonicityNot monotonic
2022-03-02T22:48:26.882927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4741337
 
3.2%
3539331
 
3.0%
3435816
 
2.8%
3235588
 
2.7%
3333430
 
2.6%
4533098
 
2.6%
4832719
 
2.5%
4632212
 
2.5%
4431035
 
2.4%
4330528
 
2.4%
Other values (73)951581
73.4%
ValueCountFrequency (%)
141318
 
0.1%
155817
 
0.4%
165104
 
0.4%
171191
 
0.1%
183901
 
0.3%
198203
 
0.6%
2016326
1.3%
2114915
1.2%
2224536
1.9%
2313209
1.0%
ValueCountFrequency (%)
96138
 
< 0.1%
95398
 
< 0.1%
941722
 
0.1%
935684
0.4%
924450
0.3%
914824
0.4%
905443
0.4%
893916
0.3%
883843
0.3%
872364
0.2%

distance
Real number (ℝ≥0)

UNIQUE

Distinct1296675
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.11247932
Minimum0.02227351335
Maximum151.8682002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2022-03-02T22:48:26.965927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.02227351335
5-th percentile24.74989046
Q155.357839
median78.26335343
Q398.46834751
95-th percentile120.4487172
Maximum151.8682002
Range151.8459267
Interquartile range (IQR)43.11050851

Descriptive statistics

Standard deviation29.0926998
Coefficient of variation (CV)0.3822329802
Kurtosis-0.6310697847
Mean76.11247932
Median Absolute Deviation (MAD)21.43870913
Skewness-0.2382690705
Sum98693149.12
Variance846.3851815
MonotonicityNot monotonic
2022-03-02T22:48:27.041930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78.773820751
 
< 0.1%
85.450670881
 
< 0.1%
73.476253371
 
< 0.1%
130.21861981
 
< 0.1%
76.572052321
 
< 0.1%
82.918208811
 
< 0.1%
90.019450011
 
< 0.1%
85.985379441
 
< 0.1%
77.89086011
 
< 0.1%
59.787792351
 
< 0.1%
Other values (1296665)1296665
> 99.9%
ValueCountFrequency (%)
0.022273513351
< 0.1%
0.066731234161
< 0.1%
0.094057725941
< 0.1%
0.11338557741
< 0.1%
0.13719950711
< 0.1%
0.15387619041
< 0.1%
0.20049591651
< 0.1%
0.20301574831
< 0.1%
0.22184622871
< 0.1%
0.25115673881
< 0.1%
ValueCountFrequency (%)
151.86820021
< 0.1%
150.58019161
< 0.1%
149.61012711
< 0.1%
149.20557141
< 0.1%
148.62367171
< 0.1%
148.52833651
< 0.1%
148.42708441
< 0.1%
148.03490771
< 0.1%
147.96467261
< 0.1%
147.95504411
< 0.1%

amt_group
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
above_medium
324151 
medium
324087 
low
194530 
high
194454 
very_low
129795 

Length

Max length12
Median length6
Mean length7.250098907
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlow
2nd rowhigh
3rd rowvery_high
4th rowmedium
5th rowmedium

Common Values

ValueCountFrequency (%)
above_medium324151
25.0%
medium324087
25.0%
low194530
15.0%
high194454
15.0%
very_low129795
10.0%
very_high129658
 
10.0%

Length

2022-03-02T22:48:27.115926image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-02T22:48:27.159926image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
above_medium324151
25.0%
medium324087
25.0%
low194530
15.0%
high194454
15.0%
very_low129795
10.0%
very_high129658
 
10.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

age_group
Categorical

HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing1318
Missing (%)0.1%
Memory size1.2 MiB
24_34
278361 
34_44
271553 
44_54
269899 
54_64
163629 
below_24
109603 
Other values (4)
202312 

Length

Max length8
Median length5
Mean length5.255077944
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row24_34
2nd row34_44
3rd row54_64
4th row44_54
5th row24_34

Common Values

ValueCountFrequency (%)
24_34278361
21.5%
34_44271553
20.9%
44_54269899
20.8%
54_64163629
12.6%
below_24109603
 
8.5%
64_74104141
 
8.0%
74_8457671
 
4.4%
84_9439964
 
3.1%
above_94536
 
< 0.1%
(Missing)1318
 
0.1%

Length

2022-03-02T22:48:27.218926image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-02T22:48:27.265926image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
24_34278361
21.5%
34_44271553
21.0%
44_54269899
20.8%
54_64163629
12.6%
below_24109603
 
8.5%
64_74104141
 
8.0%
74_8457671
 
4.5%
84_9439964
 
3.1%
above_94536
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-03-02T22:48:07.874091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:46:25.139737image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:46:33.553036image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:46:39.844592image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:46:47.832099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:46:54.416105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:47:00.998110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:47:07.311118image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:47:13.906124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:47:20.971579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:47:27.277587image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:47:33.774590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:47:40.410680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:47:47.120688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:47:54.196693image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:48:00.371085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:48:08.328092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:46:25.661740image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:46:33.911039image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:46:40.328415image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:46:48.224100image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:46:54.809105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:47:01.378111image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:47:07.702120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:47:14.311125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-03-02T22:46:38.847634image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:46:46.806095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:46:53.486104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:47:00.091109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:47:06.394119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:47:12.961123image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:47:20.062130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:47:26.403587image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:47:32.847589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:47:39.359680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:47:46.173686image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:47:53.086689image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:47:59.464084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:48:06.854091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:48:14.297093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:46:33.040038image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:46:39.223635image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:46:47.300099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:46:53.887100image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:47:00.488110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:47:06.774119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:47:13.360125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:47:20.471582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:47:26.776587image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:47:33.259592image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:47:39.822677image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:47:46.573683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:47:53.668694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:47:59.826084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-03-02T22:48:07.278090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-03-02T22:48:27.341977image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-02T22:48:27.464973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-02T22:48:27.584977image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-02T22:48:27.696977image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-03-02T22:48:27.795979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-03-02T22:48:14.907097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-03-02T22:48:17.020785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-03-02T22:48:21.152858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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02019-01-01 00:00:182703186189652095fraud_Rippin,_Kub_and_Mannmisc_net4.97F561 Perry CoveMoravian_FallsNC2865436.0788-81.17813495Psychologist, counselling0b242abb623afc578575680df30655b936.011293-82.0483150Jennifer Banks(36.0788, -81.1781)(36.011293, -82.048315)20191110013178.773821low24_34
12019-01-01 00:00:44630423337322fraud_Heller,_Gutmann_and_Ziemegrocery_pos107.23F43039 Riley Greens Suite 393OrientWA9916048.8878-118.2105149Special educational needs teacher1f76529f8574734946361c461b024d9949.159047-118.1864620Stephanie Gill(48.8878, -118.2105)(49.159047, -118.186462)20191110014130.216618high34_44
22019-01-01 00:00:5138859492057661fraud_Lind-Buckridgeentertainment220.11M594 White Dale Suite 530Malad_CityID8325242.1808-112.26204154Nature conservation officera1a22d70485983eac12b5b88dad1cf9543.150704-112.1544810Edward Sanchez(42.1808, -112.262)(43.150704, -112.154481)201911100157108.102912very_high54_64
32019-01-01 00:01:163534093764340240fraud_Kutch,_Hermiston_and_Farrellgas_transport45.00M9443 Cynthia Court Apt. 038BoulderMT5963246.2306-112.11381939Patent attorney6b849c168bdad6f867558c3793159a8147.034331-112.5610710Jeremy White(46.2306, -112.1138)(47.034331, -112.561071)20191110115295.685115medium44_54
42019-01-01 00:03:06375534208663984fraud_Keeling-Cristmisc_pos41.96M408 Bradley RestDoe_HillVA2443338.4207-79.462999Dance movement psychotherapista41d7549acf90789359a9aa5346dcb4638.674999-78.6324590Tyler Garcia(38.4207, -79.4629)(38.674999, -78.632459)20191110313377.702395medium24_34
52019-01-01 00:04:084767265376804500fraud_Stroman,_Hudson_and_Erdmangas_transport94.63F4655 David IslandDublinPA1891740.3750-75.20452158Transport planner189a841a0a8ba03058526bcfe566aab540.653382-76.1526670Jennifer Conner(40.375, -75.2045)(40.653382, -76.15266700000001)20191110415886.097358high54_64
62019-01-01 00:04:4230074693890476fraud_Rowe-Vandervortgrocery_net44.54F889 Sarah Station Suite 624HolcombKS6785137.9931-100.98932691Arboriculturist83ec1cc84142af6e2acf10c44949e72037.162705-100.1533700Kelsey Richards(37.9931, -100.9893)(37.162705, -100.15337)201911104126118.094855medium24_34
72019-01-01 00:05:086011360759745864fraud_Corwin-Collinsgas_transport71.65M231 Flores Pass Suite 720EdinburgVA2282438.8432-78.60036018Designer, multimedia6d294ed2cc447d2c71c7171a3d54967c38.948089-78.5402960Steven Williams(38.8432, -78.6003)(38.948089, -78.540296)20191110517212.754714above_medium64_74
82019-01-01 00:05:184922710831011201fraud_Herzog_Ltdmisc_pos4.27F6888 Hicks Stream Suite 954ManorPA1566540.3359-79.66071472Public affairs consultantfc28024ce480f8ef21a32d64c93a29f540.351813-79.9581460Heather Chase(40.3359, -79.6607)(40.351813, -79.958146)20191110517825.333883low74_84
92019-01-01 00:06:012720830304681674fraud_Schoen,_Kuphal_and_Nitzschegrocery_pos198.39F21326 Taylor Squares Suite 708ClarksvilleTN3704036.5220-87.3490151785Pathologist3b9014ea8fb80bd65de0b1463b00b00e37.179198-87.4853810Melissa Aguilar(36.522, -87.34899999999999)(37.179198, -87.485381)20191110614573.939714very_high44_54

Last rows

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12966652020-06-21 12:08:42213193596103206fraud_Gulgowski_LLChome72.17M7369 Gabriel TunnelPointe_Aux_PinsMI4977545.7549-84.447095Electrical engineer108c103b26f686c24c021aaf4210977e44.938461-83.9962340James Hunt(45.7549, -84.447)(44.938461, -83.996234)20206252112862697.371601above_medium24_34
12966662020-06-21 12:09:224587657402165341815fraud_Hyatt,_Russel_and_Gleichnerhealth_fitness7.30F6296 John Keys Suite 858Pembroke_TownshipIL6095841.0646-87.59172135Psychotherapist, child37a18c6fb0c5c722b6339ffedc82f55a40.556811-88.0923390Amber Lewis(41.0646, -87.5917)(40.556811, -88.092339)20206252112961670.456765lowbelow_24
12966672020-06-21 12:10:564822367783500458fraud_Hahn,_Douglas_and_Schowaltertravel19.71M97070 Anderson LandHaines_CityFL3384428.0758-81.592933804Exercise physiologist34e72e0a659a6c8f4a20ee65594f3a7d27.465871-81.5118040Christopher Farrell(28.0758, -81.5929)(27.465871000000003, -81.511804)202062521121062968.060786medium24_34
12966682020-06-21 12:11:23213141712584544fraud_Metz,_Russel_and_Metzkids_pets100.85F742 Oneill ShoreFlorenceMS3907332.1530-90.121719685Fine artist0d86d8c17638d7eff77db9c6a878b47731.377697-90.5284500Margaret Curtis(32.153, -90.1217)(31.377697, -90.52845)202062521121163694.208072high34_44
12966692020-06-21 12:11:364400011257587661852fraud_Stiedemann_Incmisc_pos37.38F474 Allen HavenNorth_LoupNE6885941.4972-98.7858509Nurse, children's9a7ea2625cf8303efe34e3c09546868f41.728638-99.0396600Marissa Powell(41.4972, -98.7858)(41.728638, -99.03966)202062521121164033.293541medium34_44
12966702020-06-21 12:12:0830263540414123fraud_Reichel_Incentertainment15.56M162 Jessica Row Apt. 072HatchUT8473537.7175-112.4777258Geoscientist440b587732da4dc1a6395aba5fb4166936.841266-111.6907650Erik Patterson(37.7175, -112.4777)(36.841266, -111.69076499999998)2020625211212659119.696415medium54_64
12966712020-06-21 12:12:196011149206456997fraud_Abernathy_and_Sonsfood_dining51.70M8617 Holmes Terrace Suite 651TuscaroraMD2179039.2667-77.5101100Production assistant, television278000d2e0d2277d1de2f890067dcc0a38.906881-78.2465280Jeffrey White(39.2667, -77.5101)(38.906881, -78.246528)202062521121264175.202184above_medium34_44
12966722020-06-21 12:12:323514865930894695fraud_Stiedemann_Ltdfood_dining105.93M1632 Cohen Drive Suite 639High_Rolls_Mountain_ParkNM8832532.9396-105.8189899Naval architect483f52fe67fabef353d552c1e662974c33.619513-105.1305290Christopher Castaneda(32.9396, -105.8189)(33.619513, -105.130529)202062521121265398.987927high44_54
12966732020-06-21 12:13:362720012583106919fraud_Reinger,_Weissnat_and_Strosinfood_dining74.90M42933 Ryan UnderpassMandersonSD5775643.3526-102.54111126Volunteer coordinatord667cdcbadaaed3da3f4020e83591c8342.788940-103.2411600Joseph Murray(43.3526, -102.5411)(42.78894, -103.24116)202062521121364084.688356above_medium34_44
12966742020-06-21 12:13:374292902571056973207fraud_Langosh,_Wintheiser_and_Hyattfood_dining4.30M135 Joseph MountainsSulaMT5987145.8433-113.8748218Therapist, horticultural8f7c8e4ab7f25875d753b422917c98c946.565983-114.1861100Jeffrey Smith(45.8433, -113.8748)(46.565983, -114.18611)202062521121362583.845902low24_34